Upload app.py

app.py

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+import streamlit as st 
+
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import cv2
+from huggingface_hub import from_pretrained_keras
+
+
+try:
+    model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
+except:
+    model=tf.keras.models.load_model("dental_xray_seg.h5")
+    pass
+    
+st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
+
+examples=["107.png","108.png","109.png"]
+link='Check Out Our Github Repo ! [link](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)'
+st.markdown(link,unsafe_allow_html=True)
+
+
+def load_image(image_file):
+	img = Image.open(image_file)
+	return img
+
+def convert_one_channel(img):
+    #some images have 3 channels , although they are grayscale image
+    if len(img.shape)>2:
+        img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        return img
+    else:
+        return img
+    
+def convert_rgb(img):
+    #some images have 3 channels , although they are grayscale image
+    if len(img.shape)==2:
+        img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)  
+        return img
+    else:
+        return img
+    
+    
+st.subheader("Upload Dental Panoramic X-ray Image Image")
+image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
+
+
+col1, col2, col3 = st.columns(3)
+with col1:
+    ex=load_image(examples[0])
+    st.image(ex,width=200)
+    if st.button('Example 1'):
+        image_file=examples[0]
+
+with col2:
+    ex1=load_image(examples[1])
+    st.image(ex1,width=200)
+    if st.button('Example 2'):
+        image_file=examples[1]
+
+
+with col3:
+    ex2=load_image(examples[2])
+    st.image(ex2,width=200)
+    if st.button('Example 3'):
+        image_file=examples[2]
+    
+    
+if image_file is not None:
+
+      img=load_image(image_file)
+      
+      st.text("Making A Prediction ....")
+      st.image(img,width=850)
+      
+      img=np.asarray(img)
+  
+      img_cv=convert_one_channel(img)
+      img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
+      img_cv=np.float32(img_cv/255)
+      
+      img_cv=np.reshape(img_cv,(1,512,512,1))
+      prediction=model.predict(img_cv)
+      predicted=prediction[0]
+      predicted = cv2.resize(predicted, (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+      mask=np.uint8(predicted*255)# 
+      _, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+      kernel =( np.ones((5,5), dtype=np.float32))
+      mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,iterations=1 )  
+      mask=cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel,iterations=1 )
+      cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+      output = cv2.drawContours(convert_rgb(img), cnts, -1, (255, 0, 0) , 3)
+
+
+      if output is not None :      
+          st.subheader("Predicted Image")  
+          st.write(output.shape)
+          st.image(output,width=850)
+
+      st.text("DONE ! ....")